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Production-deployed AImodels need a robust and continuous performance evaluation mechanism. This is where an AI feedback loop can be applied to ensure consistent model performance. But, with the meteoric rise of Generative AI , AImodel training has become anomalous and error-prone.
Two of the most important concepts underlying this area of study are concept drift vs datadrift. These phenomena manifest when certain factors alter the statistical properties of model inputs or outputs. The causes of concept drift are diverse and depend on the underlying context of the application or use case.
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